Five critical quality criteria for artificial intelligence-based prediction models

被引：27

作者：

Van Royen, Florien S. ^{[1
]}

Asselbergs, Folkert W. ^{[2
,3
]}

Alfonso, Fernando ^{[4
]}

Vardas, Panos ^{[5
]}

Van Smeden, Maarten ^{[6
,7
]}

机构：

[1] Univ Utrecht, Univ Med Ctr Utrecht, Julius Ctr Hlth Sci & Primary Care, Dept Gen Practice & Nursing Sci, Utrecht, Netherlands

[2] Univ Amsterdam, Amsterdam Univ Med Ctr, Dept Cardiol, Amsterdam, Netherlands

[3] UCL, Hlth Data Res UK, London, England

[4] Univ Autonoma Madrid, Hosp Univ la Princesa, IIS IP CIVER CV, Dept Cardiol, Madrid, Spain

[5] Biomed Res Fdn Acad Athens BRFAA, Athens, Greece

[6] Univ Utrecht, Univ Med Ctr Utrecht, Julius Ctr Hlth Sci & Primary Care, Dept Epidemiol & Hlth Econ, Univ Sweg 100, NL-3584 CG Utrecht, Netherlands

[7] Univ Utrecht, Univ Med Ctr Utrecht, Julius Ctr Hlth Sci & Primary Care, Dept Data Sci & Biostat, Univ Sweg 100, NL-3584 CG Utrecht, Netherlands

来源：

EUROPEAN HEART JOURNAL | 2023年 / 44卷 / 46期

基金：

荷兰研究理事会;

关键词：

Artificial intelligence; Digital health; Prediction; Prognosis; Diagnosis; DIGITAL HEALTH; DIAGNOSIS; CARE;

D O I：

10.1093/eurheartj/ehad727

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

To raise the quality of clinical artificial intelligence (AI) prediction modelling studies in the cardiovascular health domain and thereby improve their impact and relevancy, the editors for digital health, innovation, and quality standards of the European Heart Journal propose five minimal quality criteria for AI-based prediction model development and validation studies: complete reporting, carefully defined intended use of the model, rigorous validation, large enough sample size, and openness of code and software. Graphical Abstract Five critical quality criteria for artificial intelligence (AI)-based prediction models.

引用

页码：4831 / 4834

页数：4

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